Multi-Rate Layered Operational Optimal Control for Large-Scale Industrial Processes

In large-scale process industries, one of the great challenges is to achieve optimum operation of systems with multi-time-scale property and partially unknown models. To this end, this article proposes a novel multi-rate layered operational optimal control (OOC) method, which employs lifting technique to unify the relatively fast dual-rate of basic loop layer and relatively slow single-rate of operational layer. Besides, by integrating model-based predictive control of basic loop layer with data-based actor-critic reinforcement learning (RL) of operational layer, it overcomes the difficulty of building the operational process dynamic model. The convergence of the proposed method is proved, and dense medium separation (DMS) process is taken as an application case to illustrate the effectiveness of our proposed method via a self-developed simulation platform.